TikTok is popular around the world due to its accurate video recommendations and social networking feature.
As with other social media platforms and apps, TikTok's main advantage lies in personalized algorithmic recommendations.
However, the way the TikTok recommendation system works behind the scenes has always remained a mystery to the outside world.
Today, TikTok published a post on its official blog detailing the working mechanism of the recommendation algorithm.
In a nutshell, TikTok's recommendation algorithm is built based on input factors, somewhat similar to the way YouTube measures and monitors engagement.
The way people interact with the app, the comments they post, or the accounts they follow can all affect recommendations.
For example, if someone only follows cute animal accounts and also only likes or comments on short animal-related videos, TikTok will provide more such videos.
Here is the full text of the TikTok blog:
How TikTok recommends videos for you
TikTok's mission is to inspire creativity and bring joy. We're building a global community where you can create and share authentically, discover the world, and connect with others. The For You feed is part of what enables that connection and discovery. It's central to the TikTok experience and where most of our users spend their time.
When you open TikTok and land in your For You feed, you're presented with a stream of videos curated to your interests, making it easy to find content and creators you love. This feed is powered by a recommendation system that delivers content to each user that is likely to be of interest to that particular user. Part of the magic of TikTok is that there's no one For You feed – while different people may come upon some of the same standout videos, each person's feed is unique and tailored to that specific individual.
The For You feed is one of the defining features of the TikTok platform, but we know there are questions about how recommendations are delivered to your feed. In this post we'll explain the recommendation system behind the For You feed, discuss how we work to counter some of the issues that all recommendation services can grapple with, and share tips for how you can personalize your discovery experience on TikTok.
The basics about recommendation systems
Recommendation systems are all around us. They power many of the services we use and love every day. From shopping to streaming to search engines, recommendation systems are designed to help people have a more personalized experience.
In general, these systems suggest content after taking into account user preferences as expressed through interactions with the app, like posting a comment or following an account. These signals help the recommendation system gauge the content you like as well as the content you'd prefer to skip.
What factors contribute to For You?
On TikTok, the For You feed reflects preferences unique to each user. The system recommends content by ranking videos based on a combination of factors – starting from interests you express as a new user and adjusting for things you indicate you're not interested in, too – to form your personalized For You feed.
Recommendations are based on a number of factors, including things like:
User interactions such as the videos you like or share, accounts you follow, comments you post, and content you create.
Video information, which might include details like captions, sounds, and hashtags.
Device and account settings like your language preference, country setting, and device type. These factors are included to make sure the system is optimized for performance, but they receive lower weight in the recommendation system relative to other data points we measure since users don't actively express these as preferences.
All these factors are processed by our recommendation system and weighted based on their value to a user. A strong indicator of interest, such as whether a user finishes watching a longer video from beginning to end, would receive greater weight than a weak indicator, such as whether the video's viewer and creator are both in the same country. Videos are then ranked to determine the likelihood of a user's interest in a piece of content, and delivered to each unique For You feed.
While a video is likely to receive more views if posted by an account that has more followers, by virtue of that account having built up a larger follower base, neither follower count nor whether the account has had previous high-performing videos are direct factors in the recommendation system.
Curating your personalized For You feed
Getting started
How can you possibly know what you like on TikTok when you've only just started on the app? To help kick things off we invite new users to select categories of interest, like pets or travel, to help tailor recommendations to their preferences. This allows the app to develop an initial feed, and it will start to polish recommendations based on your interactions with an early set of videos.
For users who don't select categories, we start by offering you a generalized feed of popular videos to get the ball rolling. Your first set of likes, comments, and replays will initiate an early round of recommendations as the system begins to learn more about your content tastes.
Finding more of what you're interested in
Every new interaction helps the system learn about your interests and suggest content – so the best way to curate your For You feed is to simply use and enjoy the app. Over time, your For You feed should increasingly be able to surface recommendations that are relevant to your interests.
Your For You feed isn't only shaped by your engagement through the feed itself. When you decide to follow new accounts, for example, that action will help refine your recommendations too, as will exploring hashtags, sounds, effects, and trending topics on the Discover tab. All of these are ways to tailor your experience and invite new categories of content into your feed.
Seeing less of what you're not interested in
TikTok is home to creators with many different interests and perspectives, and sometimes you may come across a video that isn't quite to your taste. Just like you can long-press to add a video to your favorites, you can simply long-press on a video and tap "Not Interested" to indicate that you don't care for a particular video. You can also choose to hide videos from a given creator or made with a certain sound, or report a video that seems out of line with our guidelines. All these actions contribute to future recommendations in your For You feed.
Addressing the challenges of recommendation engines
One of the inherent challenges with recommendation engines is that they can inadvertently limit your experience – what is sometimes referred to as a "filter bubble." By optimizing for personalization and relevance, there is a risk of presenting an increasingly homogenous stream of videos. This is a concern we take seriously as we maintain our recommendation system.
Interrupting repetitive patterns
To keep your For You feed interesting and varied, our recommendation system works to intersperse diverse types of content along with those you already know you love. For example, your For You feed generally won't show two videos in a row made with the same sound or by the same creator. We also don't recommend duplicated content, content you've already seen before, or any content that's considered spam. However, you might be recommended a video that's been well received by other users who share similar interests.
Diversifying recommendations
Diversity is essential to maintaining a thriving global community, and it brings the many corners of TikTok closer together. To that end, sometimes you may come across a video in your feed that doesn't appear to be relevant to your expressed interests or have amassed a huge number of likes. This is an important and intentional component of our approach to recommendation: bringing a diversity of videos into your For You feed gives you additional opportunities to stumble upon new content categories, discover new creators, and experience new perspectives and ideas as you scroll through your feed.
By offering different videos from time to time, the system is also able to get a better sense of what's popular among a wider range of audiences to help provide other TikTok users a great experience, too. Our goal is to find balance between suggesting content that's relevant to you while also helping you find content and creators that encourage you to explore experiences you might not otherwise see.
Safeguarding the viewing experience
Our recommendation system is also designed with safety as a consideration. Reviewed content found to depict things like graphic medical procedures or legal consumption of regulated goods, for example – which may be shocking if surfaced as a recommended video to a general audience that hasn't opted in to such content – may not be eligible for recommendation. Similarly, videos that have just been uploaded or are under review, and spam content such as videos seeking to artificially increase traffic, also may be ineligible for recommendation into anyone's For You feed.
Improving For You
Developing and maintaining TikTok's recommendation system is a continuous process as we work to refine accuracy, adjust models, and reassess the factors and weights that contribute to recommendations based on feedback from users, research, and data. We are committed to further research and investment as we work to build in even more protections against the engagement bias that can affect any recommendation system.
This work spans many teams – including product, safety, and security – whose work helps improve the relevance of the recommendation system and its accuracy in suggesting content and categories you're more likely to enjoy.
Ultimately, your For You feed is powered by your feedback: the system is designed to continuously improve, correct, and learn from your own engagement with the platform to produce personalized recommendations that we hope inspire creativity and bring joy with every refresh of your For You feed.
Note: At the TikTok Transparency Center in Los Angeles, invited experts will have the opportunity to learn how our algorithm operates along with reviewing TikTok source code, which will be made available at the center for testing and evaluation.
